1. Field of the Invention
This invention relates generally to systems for monitoring patients during infusion of anesthesia, and more particularly, to a model structure that captures basic characteristics in patients' responses to anesthesia and surgical stimulation, such as BIS-based responses.
2. Description of the Related Art
The goals of general anesthesia are to achieve hypnosis, analgesia, and patient immobility simultaneously throughout a surgical operation while maintaining the vital functions of the body. One of the most critical tasks of anesthesia is to attain an adequate anesthetic depth.
Traditionally, anesthesia depth has been deduced indirectly from physiological signals, including autonomic responses such as heart rate, blood pressure, tearing, and patient's movements in response to surgical stimulations. The BIS monitor, which is based on the bi-spectrum level of an EEG signal, provides a viable direct measurement of anesthesia depth. The BIS index ranges from 0 to 100, where 0 corresponds to a flat line EEG, and 100 is a fully awakened state. Deep sedation is present at 60 and below, where the patient does not respond to verbal stimulus and has low probability of explicit recall. As a result of this technological advance, there have been many attempts to apply control methodologies to assist or automate the drug infusion. These preliminary studies on computer-aided drug infusion have been developed with the use of simple control strategies such as fuzzy logic or lookup tables.
To facilitate further control strategy development for improved anesthesia performance, it is desirable to develop representative models of BIS responses to drug infusion. Such models will allow more substantial and faster development and testing of new control, signal processing and decision methodologies. Also, they will provide a non-risky platform to test performance, robustness, and safety under extreme conditions and rare events. A satisfactory modeling method for this application must address several unique and challenging issues. Unlike electrical, mechanical, and chemical processes that are often repeatable and data rich, patients differ dramatically in metabolism and pre-existing medical conditions, as well as their responses to the contemplated surgical procedures. Consequently, individualized models must be established for each patient with limited patient information and data points. The expert knowledge of anesthesiologists plays critical roles in predetermining patient response characteristics. Thus, there is a need for a model that is sufficiently simple for easy clinical utility, suitable for development and testing of control methods for improved performance, and capable of incorporating expert knowledge when the data are insufficient.
The research effort to develop computer-aided drug infusion systems has been both intensive and extensive since the early 1950s. The control methodologies employed include simple control (e.g., programmed control, relay control, and PID control), adaptive control (e.g., self-tuning control, self-organizing control, and dual mode controller), and intuitive or intelligent control (e.g., fuzzy control, neural network control, and expert system-based control). To measure anesthesia depth, many indices from the EEG signals have been proposed, including the median frequency, spectral edge frequency, and auditory evoked potentials. More recently, there have been provided in the art BIS monitors having claimed medical and economic benefits including reduction in hypnotic drug usage, earlier awakening, faster time to meet post-anesthesia care unit discharge criteria, better global recovery score, and more accurate assessment of awareness risk.
It is a great challenge to characterize mathematically a patient's response to drug infusion. As a result of large deviations in the aforestated physical conditioning, age, metabolism, pre-existing medical conditions, and responses to surgical procedures among various patients, there is a demonstrable high non-linearity and large variation in their responses to drug infusion. Physiology-oriented models are usually too complicated to establish individually using limited clinical data from a patient. On the other hand, anesthesiologists have been administering drug infusion successfully with only limited information on patients, such as weight and medical condition(s). The control strategies of an experienced anesthesiologist are based substantially intuitively on basic characteristics, such as the sensitivity of the patient to a drug infusion. There is, therefore, a need for a system that provides objective assistance to the anesthesiologist, thereby reducing the medical professional's reliance on subjective criteria.
It is, therefore, an object of this invention to provide a system that provides predictive information of a patient's response to anesthesia and surgical stimulation.
It is another object of this invention to provide a system that enables predictive control to compensate for surgical stimulation.
It is also an object of this invention to provide a system that provides visual indication of the predicted impact of a drug infusion decision on anesthesia depth and corresponding physiological variable.
It is a further object of this invention to provide a system that provides indication to an anesthesiologist of impact to the status of a patient that is anticipated from surgical stimulation from predetermined aspects of a surgical operation, such as incision and closing.
It is additionally an object of this invention to provide a system that signals to an anesthesiologist a warning of impending undesirable or critical patient conditions.
It is yet a further object of this invention to provide a system that utilizes real-time data in combination with the anesthesiologist's assessment to identify predetermined characteristics that are particular to a patient.
It is also another object of this invention to provide a system that facilitates optimization of drug dosage to achieve a desired patient status during surgery.